Using customer profiling and analytics to understand customer environment and workload complexity and characteristics by industry

ABSTRACT

Aspects of the present invention include a method, system and computer program product. The method includes a processor identifying a plurality of workload characteristics by customer industry; identifying one or more workload characteristics within a customer industry; creating a test workload execution model; determining that a peak customer test coverage is beneficial to at least one customer in other customer industries; and reducing, by the processor, a number of test resources allocated to at least one customer in other customer industries.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation application of U.S. patent application Ser. No. 15/259,168 filed on Sep. 8, 2016, the contents of which are incorporated herein by reference.

BACKGROUND

The present invention relates to the testing of software, hardware, firmware, and/or other disciplines, and more specifically, to a method, system and computer program product that implement aspects of workload and operational profiling, coupled with business analytics, thereby resulting in improvements in the testing of customer software.

In the field of software testing, as in many other technical fields, improvements are constantly being sought, primarily for cost and accuracy reasons. A fundamental goal of software testing, in theory, is to identify all of the problems in a customer's software program before the program is released for use by the customer. However, in reality, this is far from the case as typically a software program is released to the customer having some number of problems that were unidentified during the software development and testing process.

A relatively more proactive approach to improving software testing is sought that employs traditional methods of understanding characteristics of clients' environments, augmented with a process of data mining empirical systems data. Such client environment and workload profiling analysis may result in software test improvements based on characteristics comparisons between the client and the test environments.

SUMMARY

According to one or more embodiments of the present invention, a computer-implemented method includes identifying, by a processor, a plurality of workload characteristics by customer industry; identifying, by a processor, one or more workload characteristics within a customer industry; and creating, by the processor, a test workload execution model. The method also includes determining, by the processor, that a peak customer test coverage is beneficial to at least one customer in other customer industries; and reducing, by the processor, a number of test resources allocated to at least one customer in other customer industries.

According to another embodiment of the present invention, a system includes a processor in communication with one or more types of memory, the processor configured to identify a plurality of workload characteristics by customer industry; to identify one or more workload characteristics within a customer industry, and to create a test workload execution model. The processor is further configured to determine that a peak customer test coverage is beneficial to at least one customer in other customer industries, and to reduce the number of test resources allocated to at least one customer in other customer industries.

According to yet another embodiment of the present invention, a computer program product includes a non-transitory storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method that includes identifying a plurality of workload characteristics by customer industry; identifying one or more workload characteristics within a customer industry; and creating a test workload execution model. The method also includes determining that a peak customer test coverage is beneficial to at least one customer in other customer industries; and reducing a number of test resources allocated to at least one customer in other customer industries.

Additional features and advantages are realized through the techniques of the present invention. Other embodiments and aspects of the invention are described in detail herein and are considered a part of the claimed invention. For a better understanding of the invention with the advantages and the features, refer to the description and to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter which is regarded as the invention is particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The forgoing and other features, and advantages of the invention are apparent from the following detailed description taken in conjunction with the accompanying drawings in which:

FIG. 1 depicts a cloud computing environment according to one or more embodiments of the present invention;

FIG. 2 depicts abstraction model layers according to one or more embodiments of the present invention;

FIG. 3 is a block diagram illustrating one example of a processing system for practice of the teachings herein;

FIG. 4 is a flow diagram of a method for using effective customer profiling and analytics to understand customer workload characteristics by customer industry and to focus appropriate test resources in accordance with one or more embodiments of the present invention.

DETAILED DESCRIPTION

It is understood in advance that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service's provider.

Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.

Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds).

A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure comprising a network of interconnected nodes.

Referring now to FIG. 1, illustrative cloud computing environment 50 is depicted. As shown, cloud computing environment 50 comprises one or more cloud computing nodes 10 with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone 54A, desktop computer 54B, laptop computer 54C, and/or automobile computer system 54N may communicate. Nodes 10 may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment 50 to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices 54A-N shown in FIG. 1 are intended to be illustrative only and that computing nodes 10 and cloud computing environment 50 can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser).

Referring now to FIG. 2, a set of functional abstraction layers provided by cloud computing environment 50 (FIG. 1) is shown. It should be understood in advance that the components, layers, and functions shown in FIG. 2 are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided:

Hardware and software layer 60 includes hardware and software components. Examples of hardware components include: mainframes 61; RISC (Reduced Instruction Set Computer) architecture based servers 62; servers 63; blade servers 64; storage devices 65; and networks and networking components 66. In some embodiments, software components include network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers 71; virtual storage 72; virtual networks 73, including virtual private networks; virtual applications and operating systems 74; and virtual clients 75.

In one example, management layer 80 may provide the functions described below. Resource provisioning 81 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 82 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may comprise application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal 83 provides access to the cloud computing environment for consumers and system administrators. Service level management 84 provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment 85 provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.

Workloads layer 90 provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation 91; software development and lifecycle management 92; virtual classroom education delivery 93; data analytics processing 94; transaction processing 95; and a method 96 for using effective customer profiling and analytics to understand customer workload characteristics by customer industry and to focus appropriate test resources in accordance with one or more embodiments of the present invention.

Referring to FIG. 3, there is shown a processing system 100 for implementing the teachings herein according to one or more embodiments. The system 100 has one or more central processing units (processors) 101 a, 101 b, 101 c, etc. (collectively or generically referred to as processor(s) 101). In one embodiment, each processor 101 may include a reduced instruction set computer (RISC) microprocessor. Processors 101 are coupled to system memory 114 and various other components via a system bus 113. Read only memory (ROM) 102 is coupled to the system bus 113 and may include a basic input/output system (BIOS), which controls certain basic functions of system 100.

FIG. 3 further depicts an input/output (I/O) adapter 107 and a network adapter 106 coupled to the system bus 113. I/O adapter 107 may be a small computer system interface (SCSI) adapter that communicates with a hard disk 103 and/or tape storage drive 105 or any other similar component. I/O adapter 107, hard disk 103, and tape storage device 105 are collectively referred to herein as mass storage 104. Operating system 120 for execution on the processing system 100 may be stored in mass storage 104. A network adapter 106 interconnects bus 113 with an outside network 116 enabling data processing system 100 to communicate with other such systems. A screen (e.g., a display monitor) 115 is connected to system bus 113 by display adaptor 112, which may include a graphics adapter to improve the performance of graphics intensive applications and a video controller. In one embodiment, adapters 107, 106, and 112 may be connected to one or more I/O busses that are connected to system bus 113 via an intermediate bus bridge (not shown). Suitable I/O buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters typically include common protocols, such as the Peripheral Component Interconnect (PCI). Additional input/output devices are shown as connected to system bus 113 via user interface adapter 108 and display adapter 112. A keyboard 109, mouse 110, and speaker 111 all interconnected to bus 113 via user interface adapter 108, which may include, for example, a Super I/O chip integrating multiple device adapters into a single integrated circuit.

In exemplary embodiments, the processing system 100 includes a graphics processing unit 130. Graphics processing unit 130 is a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display. In general, graphics processing unit 130 is very efficient at manipulating computer graphics and image processing, and has a highly parallel structure that makes it more effective than general-purpose CPUs for algorithms where processing of large blocks of data is done in parallel.

Thus, as configured in FIG. 3, the system 100 includes processing capability in the form of processors 101, storage capability including system memory 114 and mass storage 104, input means such as keyboard 109 and mouse 110, and output capability including speaker 111 and display 115. In one embodiment, a portion of system memory 114 and mass storage 104 collectively store an operating system to coordinate the functions of the various components shown in FIG. 3.

In accordance with one or more embodiments of the present invention, methods, systems, and computer program products are disclosed for using effective customer profiling and analytics to understand customer workload characteristics by customer industry and to focus appropriate test resources.

With reference now to FIG. 4, a flow diagram illustrates a method 200 according to one or more embodiments of the present invention for using effective customer profiling and analytics to understand customer workload characteristics and complexity by customer industry and to focus appropriate test resources.

In one or more embodiments of the present invention, the method 200 may be embodied in software that is executed by computer elements located within a network that may reside in the cloud, such as the cloud computing environment 50 described hereinabove and illustrated in FIGS. 1 and 2. In other embodiments, the computer elements may reside on a computer system or processing system, such as the processing system 100 described hereinabove and illustrated in FIG. 3, or in some other type of computing or processing environment.

After a start operation in block 204, an operation in block 208 identifies a number of common and unique workload characteristics (including complexity) by customer industry. It also determines which customer industries have the peak test coverage requirements for individual and collective components and functional areas. This information may be stored in a database.

An operation in block 212 identifies a number of common and unique workload characteristics (including complexity) within customer industry. It also determines which customer(s) within a specific industry have the peak test coverage requirements for individual and/or collective components and functional areas, possibly identifying current and future use trends. This information may also be stored in a database.

An operation in block 216 utilizes various workload modeling technologies and methodologies to create a test workload execution model, and also various run time and historic customer profiling techniques to provide the appropriate resources to achieve test functional coverage, stress/activity levels, and/or test plan requirements. This information may also be stored in a database.

For example, a test workload execution model may be created by utilizing a body of available workload work unit activity data, data store activity data, and a workload execution distribution scheme. From this, a test workload execution can be modeled with customer data convergence as a goal. The model may be created using a visual interface. Workload models can be created, stored for others to use, and for future use. The models contain the information about the execution distribution of workload work units. Each work unit exercises a set of activities that utilize one or more data stores. By creating and storing the work unit activities by work unit and data store, one can create distribution adjustment controls such as sliders to increase or decrease the amount of an activity. Furthermore, the activities can be grouped by work unit (transaction), data store (database), main activity for a unit of work, atomic activity for a unit of work, or combo activity for multiple activities in a unit of work. With the control value and the available workload work unit data, one can adjust the workload execution distribution to match the visual model being displayed. In this way, a tester or analyst can see the change and understand if the change may help or harm convergence to a customer activity model.

The workload data required for execution distribution modeling includes the data store mapping for work unit activity and work unit mapping. An execution distribution is calculated using this information. Since each work unit activities are known, creating a distribution is as simple as a multiplication. Thus, embodiments of the present invention utilize test workload application information relating transaction activity to transaction data store, to thereby visually model the execution mix of transactions for the test workload. The resulting mix is used to create a transaction distribution to be followed by the user simulation tool such that each transaction is appropriately exercised according to the visual model.

Also, various run time and historic customer profiling techniques may include, for example, utilizing a body of available customer data to provide an analytical comparison of archived baseline customer data and live test system data on a one to one basis or one to rotating basis. The customer data may be organized, for example, by industry, to provide data such as that relating to rotating industry, maximum customer, any customer, lowest customer, maximum of a particular industry, any industry, lowest of an industry, etc. The comparison analysis may be conducted in real time during normal test runs, allowing for relatively quick discovery and mitigation of test deficiencies, thereby resulting in more productive tests.

The embodiments may include real-time detection and discovery of test workload characteristics alignment or comparison with each or all members of a set of customer production workload environments across a set of statistical measures with run-time visualization of customer baselines data versus live test system data. This is done to identify and mitigate poor test characteristics in real time.

Known workload profiling techniques involve post-processing of data from customer and test systems. However, embodiments essentially move the profiling process from post processing to real-time processing during the test execution. In the test, there is a relatively large body of customer workload data that is used to perform post-process analytics to determine any “holes” in testing. Experience has shown that testers may sometimes make workload changes that are not reviewed from a profiling perspective. When this occurs, it is easy to run with undesirable workload characteristics for long periods of time. By moving the process to test workload run time, undesirable changes or conditions can be mitigated immediately. Thus, embodiments provide for real-time system activities feedback while simultaneously providing customer profiled activities feedback. This is a relatively agile approach to customer/workload profiling.

Using the body of available customer data, a comparison of customer and live test system data may be carried out so that an agile profiling exercise can be conducted during normal test runs. Also, web interface charts and tables may be used for displays that reflect the profiling data.

One or more embodiments utilize existing customer data compared or contrasted with the live collection of test data, and then storing the data which results from the comparison operation in a database (e.g., DB2) or in some other type of memory. The resulting data may be used in a web application or in some other type of visual data display application to visually represent the levels of load and stress and ratios of activity for sets of related data points.

Typically, the load is created by the activity of one or more workloads as the driver of the system conditions of relatively high processor, I/O, Communications, Graphics, etc. (i.e., system resource) utilization. Stress is the resulting system resource constraints that cause system component interactions and timings to change where overall performance can become limited and begin to degrade. A highly stressed system is more likely to encounter problems as compared to a system with relatively low-stress levels. A relatively large difference in mainframe computing systems versus smaller computing systems is that mainframes are capable of consistently running at extremely high utilization rates—close to 100%. For this reason load and stress of the system is an important aspect of mainframe testing. An example of load and stress example is that if one scales up a system with more and faster processors and I/O devices and more memory (i.e., reduces stress on the system), then the system can handle a relatively greater load (i.e., higher workload activity levels).

The sets of related data points may be referred to as the analysis points. Analysis points each contain multiple data points that are related to one another. On the display page of the web application, the data may be fed into line, pie, and other charts/visualizations that depict the test system levels contrasted with data that rotates chart updates for different customers. This way a user can better see the customer data as compared with the test data.

An operation in block 220 utilizes various run time and historic customer profiling techniques as discussed above to determine if the peak customer test coverage is also beneficial to other customers in other industries, and/or to the global customer set. This information may be also stored in a database.

Next, a decision operation in block 224 determines if the peak customer test coverage is also beneficial to other customers in other industries, and/or to the global customer set. If not, an operation in block 228 maintains the currently allocated test plan and test resources across other customers and industries with no changes. This information may be also stored in a database. The method 200 then ends in an operation in block 232.

If it is determined in the operation in block 224 that the peak customer test coverage is also beneficial to other customers in other industries, and/or to the global customer set, an operation in block 236 reduces the test plan and resources used for coverage of other customers and/or industries covered the by peak industry customer test coverage. This information may be also stored in a database. The method 200 then ends in the operation in block 232.

Embodiments of the present invention reduce various key or important workload and environment complexities' and characteristics' comparisons to specific industries that exhibit relatively greater complexity levels or the highest levels of these targeted characteristics, when and where possible. In reducing the subset of workload and environment comparisons in this way, the corresponding required test resources and customer comparisons are reduced to a subset of the global customer set while providing coverage beyond this industry subset, up to and even encompassing the global customer set.

Given that test workload runs can be complicated, resource and time intensive, limited in availability, and financially expensive to configure, stage, run, and analyze, and can span multiple days or even weeks (including non-user monitored off-shift and weekend time), properly focusing available test resources by specific customer industries that have significantly unique workload and/or environment characteristics (including complexity) can result in much more cost effective use and financial benefits.

This understanding of customer workload and environment key characteristics (including complexity) by industry provides multiple capabilities, efficiencies, and financial benefits including: to understand the effectiveness of the workload run (software, hardware, firmware)—as viewed from a variety of perspectives, including problem determination, scalability, performance, stress/load, etc.; to tune test workloads much closer to their intended activity goals (through a wide range of factors/metrics), by only comparing to customer workloads with the target (usually highest) activity levels; to significantly reduce the amount of limited and high value System z systems, storage, network, environmental, personnel time and resources to accomplish test objectives, resulting in both financial savings and reduced environmental impact; and to increase test plan efficiency through expanded test coverage, resulting in enhanced product quality and greater customer satisfaction. By the reduction of overall test workload runs through targeted customer workload comparisons, the test user or operator can run additional and/or expanded test cases or scenarios, and ensure each workload run maximizes a successful outcome.

The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.

Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.

These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.

The following definitions and abbreviations are to be used for the interpretation of the claims and the specification. As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having,” “contains” or “containing,” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a composition, a mixture, process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but can include other elements not expressly listed or inherent to such composition, mixture, process, method, article, or apparatus.

As used herein, the articles “a” and “an” preceding an element or component are intended to be nonrestrictive regarding the number of instances (i.e., occurrences) of the element or component. Therefore, “a” or “an” should be read to include one or at least one, and the singular word form of the element or component also includes the plural unless the number is obviously meant to be singular.

As used herein, the terms “invention” or “present invention” are non-limiting terms and not intended to refer to any single aspect of the particular invention but encompass all possible aspects as described in the specification and the claims.

As used herein, the term “about” modifying the quantity of an ingredient, component, or reactant of the invention employed refers to variation in the numerical quantity that can occur, for example, through typical measuring and liquid handling procedures used for making concentrates or solutions. Furthermore, variation can occur from inadvertent error in measuring procedures, differences in the manufacture, source, or purity of the ingredients employed to make the compositions or carry out the methods, and the like. In one aspect, the term “about” means within 10% of the reported numerical value. In another aspect, the term “about” means within 5% of the reported numerical value. Yet, in another aspect, the term “about” means within 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1% of the reported numerical value.

The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. 

What is claimed is:
 1. A computer-implemented method comprising: identifying, by a processor, a plurality of workload characteristics by customer industry; identifying, by the processor, one or more workload characteristics within a customer industry; creating, by the processor, a test workload execution model; determining, by the processor, that a peak customer test coverage is beneficial to at least one customer in other customer industries; and reducing, by the processor, a number of test resources allocated to at least one customer in other customer industries.
 2. The computer-implemented method of claim 1 wherein the workload characteristics include complexity.
 3. The computer-implemented method of claim 1 further comprising determining, by the processor, that a peak customer test coverage is not beneficial to at least one customer in other customer industries.
 4. The computer-implemented method of claim 3 wherein determining, by the processor, that a peak customer test coverage is not beneficial to at least one customer in other customer industries further comprises maintaining, by the processor, a number of test resources allocated to the at least one customer in other customer industries.
 5. The computer-implemented method of claim 1 wherein creating, by the processor, a test workload execution model further comprises utilizing, by the processor, a body of available workload work unit activity data, data store activity data, and a workload execution distribution scheme.
 6. The computer-implemented method of claim 1 wherein identifying, by the processor, a plurality of workload characteristics by customer industry further comprises determining, by the processor, which customer industries have peak test coverage requirements for individual and collective components and functional areas.
 7. The computer-implemented method of claim 1 wherein identifying, by the processor, a plurality of workload characteristics within a customer industry further comprises determining, by the processor, which customer within a particular industry has peak test coverage requirements for individual and collective components and functional areas. 